Improve LP Process Sub-Models Using Linked Process Simulators v2

8/13/2019 Improve LP Process Sub-Models Using Linked Process Simulators v2

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Improve LP Process Unit Sub-models UsingLinked Process Simulators

Abstract

An important application for process unit simulators is routinely generating accurate data

for the refinery LP process unit sub-models. There is significant economic benefit from

using tuned process unit simulator models to frequently update and improve the accuracyof the refinery LP process unit sub-model representations. Recent technology advances

have made this task fast and efficient.

This paper describes several recent worldwide projects where process simulation models

have been directly linked to LP input data tables for frequent updating of the LP processunit sub-model representations. We will describe the Best Practice methods for process

unit data collection, simulation model tuning, process unit performance monitoring and

analysis, and finally, direct LP data generation and LP updates using automated simulatorruns.

Introduction

Many refiners use process simulation models for a variety of applications, including:

• Performance Monitoring – Tracking each key performance indicator (KPI).

• Optimization – Evaluating alternative operations on the simulator to maximize profit

within real-world limitations.

• “What If” Studies – Measuring the effect of unit changes from feedstock property

and operating parameter shifts.

• Catalyst selection – evaluate changes in catalyst composition

• Capital Project Evaluation – simulate de-bottlenecking studies and/or othermodifications

• Training – use the model off-line as a training tool for technical staff.

• Planning Support – Updating and maintaining LP tools.

This paper focuses on the use of process simulation models in support of LP planning andLP model maintenance activities.

Process simulation models have long been used to generate data for LP process unit sub-model representations. The process simulator may be calibrated to the commercial unit

performance data. After calibration, the process simulator model may be used to produce

case studies that provide data for LP process unit sub-model tables for a variety of LPmodel structures. This work process is particularly effective using process simulators

and kinetic models for Crude /Vacuum Units, FCCU/RFCCU’s, Cokers, Visbreakers,

Hydrocrackers, Reformers and Hydrotreaters.



In recent years, we have seen an increasing interest by refiners for improving theaccuracy of the LP sub-model representations. LP accuracy is improved by emphasizing

two aspects:

• compare and maintain LP model accuracy vis-à-vis the commercial process unithistorical performance

• use “best practice” LP model structure and modeling techniques

The focus of this paper is not advances in LP sub-model structure, however, the most

prevalent major process unit modeling techniques generally include the use of base-deltatype of LP sub-models with distributive recursion for major feed and product properties.

Rather, we will focus on the methods and advances in monitoring process units,comparing actual performance to LP-predicted, and frequently updating the LP to more

closely match the actual unit performance.

Specifically, we review recent developments that directly link process simulators to LP

tables thereby providing convenient, quick, semi-automatic updates for LP process unit

sub-model tables.

In the past, updating LP process unit sub-models has been a tedious, time-consuming,

one-off activity. As a result, it is not done very frequently. In many LP models, it has been a number of years since the major process unit sub-models have been updated or

maintained. Some process unit sub-models have never been updated since the day they

were built.

With some routine procedures, methods and automation, the process of checking andupdating the LP may be easily completed. Hence, it is likely to be completed more

frequently. This will improve and maintain LP model accuracy.

With this paper, we offer suggestions for procedures, methods and automation that

streamline and automate the process for maintaining the LP process unit sub-modelaccuracy. This information is intended for those who are involved in LP data generation

and LP maintenance activities. This may include both unit process engineers as well as

refinery planners.

We discuss the best practice methods for monitoring and LP data generation. We provide

examples from a variety of recent real-world refinery applications that illustrate themethods.



Process Unit LP Data Generation

Introduction

The purpose of this section of the report is to describe the best practice methods for

maintenance and updates for major downstream process unit LP sub-models. Most

major process unit LP sub-models include a base-delta type of structure. This structuregenerally includes one or more base yield structures. The base yield structure represents

a typical feed at a typical operating condition. A series of delta correctors represent theshifts associated with changes in the key feed properties and key operating variables.

Generally, these base and delta shifts include predictions for not only the yields but alsothe product stream properties off the unit. In many cases, the key feed properties and the

product properties are recursed (vary) using distributive recursion modeling techniques.

Considering the complexity of this modeling structure, building and maintaining the datafor this type of model can be very difficult. Described below is a general methodology

that is intended to maintain a highly accurate process unit sub-model.

General Methodology

The following steps are part of a routine procedure intended to maintain the process unit

sub-models in the LP model.

Monitoring

In this step, the refinery process engineer routinely monitors a process unit, calibrates a

simulation model, and then adds the calibration data to the historical database of

calibration data.

We believe that the best practice for most situations is to conduct a specific “snapshot”

monitoring of the unit on a weekly basis. One refiner calls this a “super sample” day. On

the selected day, the refinery maintains a typical feed and a typical operating condition onthe unit. Samples are taken and tested of all the feeds and product streams such that an

overall unit volume and mass balance can be completed and the key feed and properties

are measured. The stream sampling during this special period is typically moreextensive than that conducted on routine, daily basis but somewhat less extensive than for

a full-blown comprehensive test run. Also, unlike most test runs, the purpose is to

capture the unit during normal conditions, rather than some “special” condition.

When the lab samples are completed (usually by the next day), the process engineer

completes several tasks:



1. The operating and laboratory data is captured on spreadsheets as inputs. Usually

this may be done automatically from plant information and laboratory systemlinks.

2. The meter readings are temperature corrected.

3. The unit is overall mass balanced on spreadsheets using reconciliation techniques

(or a reconciliation program may be used).

4. Using the process simulator model for the unit, the actual performance results are

input to the calibration input section of the simulator program. A calibration run

is executed with the simulator. The resultant calibration factors are added to thehistorical calibration database for the simulator program.

5. Using the LP process unit sub-model as a standalone spreadsheet, input the actual

unit feed properties, rates and operating parameters and predict the unit yields and

stream properties. The results are added to an LP sub-model predicted yieldsdatabase file.

The whole process of data gathering, mass-balancing, LP sub-model prediction and

simulator calibration should only take a few hours each week for the unit processengineer (assuming ready access to the electronic plant data and well-developed

spreadsheets for the data calculations).

The following example shows a portion of the calibration input tab for the a RFCC

simulation model (from a KBC-Profimatics™ FCC-SIM® application). Each datacolumn (starting in column K) represents the calibration input data for a particular

monitoring day.



After the calibration input, a calibration run is executed. The output from this simulation

run appears on the Calibration Results page. A key element of this output is the

calibration factors. These calibration factors appear on the spreadsheet, but the

calibration data is automatically added to an external calibration data file.

A portion of the Calibration Results page is shown below for the RFCC application. Itshows the calculated calibration factors.



Monitoring Graphs

The refinery process engineer maintains graphs then compares the actual unit

performance against the simulator predicted performance and against the LP predicted

performance.

Following each week’s snapshot performance data collection for the unit, the monitoring

graphs are updated. The graphs contain:

1. Actual Yields and Properties – this may be extracted from the Calibration Input

spreadsheet. Usually this includes graphs for all major process unit yields andselected key properties.

2. LP Predicted Yields and Properties – a simple yield and property LP calculator

is used. The LP calculator contains the current process unit sub-model with theadded capabilities to set the feed qualities and operating conditions to match the

sampling period. The LP calculator will use matrix calculations to determine the

LP predicted yields and properties for the input data. A macro will extract thisdata onto the graphing spreadsheet.



3. Simulator Predicted Yields and Properties – using the calibration data that was

the basis for the previous LP update, a special predict run is made with thesimulator using the same feed and operating conditions.

The following is an example graph showing the yield comparisons for an FCC unit Slurry

yield reported as a volume percent of feed. The Actual curve is the historic commercialunit result, the LP curve is the predicted yield from the LP sub-model and the Petrofine

(KBC-Profimatics™ kinetic model) is the simulator results.

FCC SLURRY

0.00%

2.00%

4.00%

6.00%

8.00%

10.00%

1 3 5 7 9 1 1

1 3

1 5

1 7

1 9

2 1

2 3

2 5

2 7

2 9

Actual LP Petrofine

The refinery process engineer and the refinery LP planner discuss the performance

comparisons regularly and determine if the LP sub-model needs updating. In theaforementioned graph, the FCC Slurry actual yield has been significantly higher than the

LP predictions.

LP Process Unit Sub-Model Update

The planner has a special LP data generation application for the simulation model. The

planner first selects the particular calibration data to include in the average calibrationdata for the LP update. It may be appropriate to exclude some calibration data from the

average or to select a particular period for the average calibration data. The monitoring

graphs will be helpful in determining which data and what periods should be used as the

updated simulator calibration factors.

The following shows the calculation of the average calibration factors based on the user-selected calibration data.



The newly calibrated process simulator is then used to execute a pre-determined series of

simulator cases. These cases are designed to provide the necessary data for the base-delta

LP calculations. In this example, approximately 25 simulator runs were required to produce the base case and the deltas for feed property correctors, operating shifts and cut-

point shifts associated with the LP sub-model structure.

These 25 runs are all set up in the Predict Input section of the simulator application,

shown below. It typically takes about 10 minutes to automatically execute the series ofcases.



The results from the execution of these simulator cases appear on the Predict Result

spreadsheet on the simulator workbook, as shown below.



The special data generating application includes spreadsheets that use the simulator

results to calculate the necessary coefficients for the LP sub-model table. The following

shows a portion of the calculations for the LP table.



The LP sub-model table is updated automatically based on the cascading of calculations

from the calibrated simulator runs, through the LP calculations sheet, and finally, into the

LP sub-model table.

The following is a portion of the LP sub-model table. All of the coefficients in this table

are based on the simulator run results and calculations. This includes all of the baseyields and properties as well as the delta (shifted) yields and properties.



The updated LP sub-model table is extracted (as values) and combined in a new version

of the refinery LP model. The model is tested and implemented into the planning work

processes.

Once a decision is made to update the LP sub-model, the process of selecting thecalibration data, executing the calibration runs and the predict runs, and extracting the LP

sub-model takes less than an hour.

The aforementioned methods may be used for a significant number of major refining

processes including crude and vacuum distillation processes, Cracking Units (FCC and

RFCC), Reforming Units, Cokers, Visbreakers, Hydrotreaters, Hydrocrackers, etc.

Benefits

The performance of major refinery process units change frequently. Mechanical and

catalyst changes during unit turnarounds as well as gradual performance deterioration

between turnarounds are typical causes. The monitoring process (with graphs) may beused to determine the accuracy of the current LP sub-models versus the actual recent

performance of the unit.



If the LP update process is easy, convenient and automatic, it is likely that the updates

will be more frequent and will be performed when needed.

Accuracy improvements may be easily illustrated using the monitoring graphs.

Following LP upgrades, the LP predicted results that show a much better correlation to

the actual results than before the upgrade. These shifts and the associated improvementsare easily identified on the graphs.

Some refiners calculate the economic benefit of these LP upgrades by comparing the“old” model objective functions with the “new” model objective functions using the same

case. The difference is an indicator of the economic value of the accuracy improvements.

If LP updates are regular and frequent, the value of any particular sub-model upgrade

may be relatively small. However, the real value comes from the frequent maintenance

of the sub-models. The monitoring and routine maintenance of the LP means that a

serious overall degradation of the LP model is avoided. Better models and superior

planning processes lead to profit improvements for the refiner.

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Improve LP Process Sub-Models Using Linked Process Simulators v2